Deep redatuming for PDE and inverse problems

Speaker: Prof. Dr. Laurent Demanet
Affiliation: MIT – Massachusetts Institute of Technology (USA)
Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Erlangen-Nürnberg (Germany)

Zoom meeting link
Meeting ID: 667 5616 1029 | PIN: 387887

Abstract. Neural networks have been leveraged in non-trivial ways in the context of computational inverse problems over the past few years. In this talk, I will explain how deep nets can sometimes generate helpful “virtual” (unobserved) solutions of PDE, when all is known about the PDE is a set of other (observed) solutions. This estimation task is dubbed deep redatuming, and extends the reach of inversion in substantive ways. I will discuss one example in particular: symmetric autoencoders, where symmetries are the driving principle behind scientific prediction. The mathematical context for this example is a ubiquitous form of “latent rank-1” structure, which has not been formalized yet, but which is found throughout nature. Joint work with Hongyu Sun, Brindha Kanniah, Pawan Bharadwaj, and Matt Li.

This event on LinkedIn

The event is finished.

No Responses

© 2019-2022 FAU DCN-AvH Chair for Dynamics, Control and Numerics - Alexander von Humboldt Professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany | Imprint | Contact